Record 12012026 · captured 2026-08-25
The world looked up Bob Weir. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
Robert Hall Weir was an American musician and songwriter best known as a founding member of the Grateful Dead. After the group disbanded in 1995, he performed with the Other Ones, later known as the Dead, together with other former members of the Grateful Dead
The RajaSaab is a 2026 Indian Telugu-language fantasy horror comedy film written and directed by Maruthi, and produced by People Media Factory and IVY Entertainment. The film stars Prabhas, alongside Sanjay Dutt, Nidhhi Agerwal, Malavika Mohanan, Riddhi Kumar
His & Hers is an American mystery thriller limited series starring Tessa Thompson, Jon Bernthal, Pablo Schreiber, Marin Ireland, Sunita Mani, Rebecca Rittenhouse, Chris Bauer, Poppy Liu and Crystal Fox. It is an adaptation of the 2020 novel of the same name by
Caleb Williams is an American professional football quarterback for the Chicago Bears of the National Football League (NFL). Following one season of college football with the Oklahoma Sooners, he played for the USC Trojans and won the Heisman Trophy in 2022 af
The Grateful Dead was an American rock band formed in Palo Alto, California, in 1965. Known for their eclectic style that fused elements of rock, blues, jazz, folk, country, bluegrass, rock and roll, gospel, reggae, and world music with psychedelia, the band i
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
Avatar: Fire and Ash is a 2025 American epic science fiction film directed by James Cameron from a screenplay he co-wrote with Rick Jaffa and Amanda Silver. Produced by Lightstorm Entertainment, it is the third installment in the Avatar film series and the seq
Timothy Busfield is an American actor and director. He played Arnold Poindexter in the first two Revenge of the Nerds films, Elliot Weston on the television series Thirtysomething, Mark in Field of Dreams, and Danny Concannon on the television series The West
Parasakthi is a 2026 Indian Tamil-language political action drama film directed by Sudha Kongara and produced by Aakash Baskaran of Dawn Pictures. The film stars Ravi Mohan, Sivakarthikeyan, Atharvaa and Sreeleela. Set mainly in 1964, it follows two brothers p
Thomas Kent Carter was an American actor best known for his roles in the films Corvette Summer (1978), Southern Comfort (1981), The Thing (1982), Doctor Detroit (1983), Runaway Train (1985), Space Jam (1996) and The Corner (2000), as well as for the TV series
The 2025–2026 Iranian protests were a series of nationwide demonstrations against the government of Iran that began on 28 December 2025 amid a deepening economic crisis. The unrest followed a sharp depreciation of the Iranian rial, rising inflation, and widesp
Reza Pahlavi is an Iranian political activist and the former Crown Prince of the Pahlavi dynasty of Iran. He is the eldest son of Mohammad Reza Pahlavi, the last Shah of Iran, and his wife, Empress Farah. He lives in the United States as a dissident in exile.
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
On January 7, 2026, Renée Nicole Macklin Good, a 37-year-old American woman, was fatally shot by United States Immigration and Customs Enforcement (ICE) agent Jonathan Ross in Minneapolis, Minnesota, during Operation Metro Surge. Good was in her car stopped si
Joshua Patrick Allen is an American professional football quarterback for the Buffalo Bills of the National Football League (NFL). He is regarded as one of the greatest dual-threat quarterbacks of all time and is the NFL leader in quarterback rushing touchdown
Heated Rivalry is a Canadian sports romance television series created, written, and directed by Jacob Tierney for Crave. Based on the Game Changers book series by Rachel Reid, the show takes its title from the 2019 second installment. It stars Hudson Williams
William Trevor Lawrence is an American professional football quarterback for the Jacksonville Jaguars of the National Football League (NFL). Considered among the highest-touted college football prospects, he won the 2019 National Championship Game as a freshma
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Matthew Robert LaFleur is an American professional football coach and former player who is the head coach for the Green Bay Packers of the National Football League (NFL). He previously was the offensive coordinator for the Los Angeles Rams and the Tennessee Ti
Derek William Rapp, known profesionally as Derek Martin, was an English actor. With a career spanning more than five decades, he began as a stuntman before moving into acting, appearing in a range of British television roles, which included starring in the cri
Prashant Tamang was an Indian singer and film actor based in Kathmandu. He was the winner of Indian Idol Season 3 in 2007.
Mohammad Reza Pahlavi was the last Shah of Iran, reigning from 1941 to 1979. He succeeded his father Reza Shah and ruled the Imperial State of Iran until he was overthrown in the Islamic Revolution led by Ruhollah Khomeini, which abolished the Iranian monarchy
People We Meet on Vacation (film)
People We Meet on Vacation is a 2026 American romantic comedy film directed by Brett Haley from a screenplay by Yulin Kuang, Amos Vernon, and Nunzio Randazzo, based on the 2021 novel by Emily Henry. It stars Emily Bader and Tom Blyth.
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Iran, officially the Islamic Republic of Iran, and historically known as Persia, is a country in West Asia. It borders Iraq to the west, Turkey, Azerbaijan, and Armenia to the northwest, the Caspian Sea to the north, Turkmenistan to the northeast, Afghanistan
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
Stranger Things is an American television series created by the Duffer Brothers for Netflix. Produced by Monkey Massacre Productions and 21 Laps Entertainment, the first season was released on Netflix on July 15, 2016. The second and third seasons followed in
The Night Manager (British TV series)
The Night Manager is a British spy thriller television serial based on the 1993 novel by John le Carré and adapted by David Farr. The six-part first series, directed by Susanne Bier and starring Tom Hiddleston, Hugh Laurie, Olivia Colman, Tom Hollander, David
Melissa Ellen Gilbert is an American actress. Gilbert began her career as a child actress in the late 1960s, appearing in numerous commercials and guest-starring roles on television. From 1974 to 1983, she starred as Laura Ingalls Wilder, the second-oldest dau
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Representation learning on electronic health records (EHRs) plays a vital role in downstream medical prediction tasks. Although natural language processing techniques, such as recurrent neural networks, and self-attention, have been adapted for learning medical representations from hierarchical, time-stamped EHR data, they often struggle when either general or task-specific data are limited. Recent efforts have attem
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
Deep convolutional neural networks (Deep CNN) have achieved hopeful performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose an approach called SDT-DCSCN that jointly performs super-resolution and deblurring of low-resolution blurry text images based o
Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models
In a mixed generalized linear model, the goal is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular class of estimators which output the top two eigenvectors of
Utilising physics-guided deep learning to overcome data scarcity
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to
An Evaluation on Large Language Model Outputs: Discourse and Memorization
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies such as counterfactual and logically-flawed statements, and
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presence of instance-dependent noise (IDN), a realistic form of label noise arising from ambiguous sample information. To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exem
Simple Mechanisms for Representing, Indexing and Manipulating Concepts
Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a latent generative process that produces the data, and the generative process is often hierarchical, involving latent concepts. Despite the significant work on understanding the learning of the latent structure and underlying concepts using t
Expression Syntax Information Bottleneck for Math Word Problems
Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes. Traditional deep-learning based methods for traffic flow forecasting typically rely on historical data to train their models, which are then used to make predictions on future data. However, the performance of the trained model usually degr
Simulating Multi-Stakeholder Decision-Making with Generative Agents in Urban Planning
Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the development of multi-generative agent systems, offering a promising a
Dynamic and Adaptive Feature Generation with LLM
The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature engineering. As one of the most important techniques, feature generation transforms raw data into an optimized feature space conducive to model training and further refines the space. Despi
Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty
Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essentia
Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-centric, conversation
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection
We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, using hallucination pattern guidance and a language style alignment during generation. Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the st
This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset
Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks
Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunately, current testing methods for QNNs remain underdeveloped, with limited practical utility and insufficient empirical evaluation. As an initial effort, we design a set o
AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning
In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of ``slow thinking'' into multimodal large language models (MLLMs). Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity. We propose a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic s
Infrared and visible (IR-VIS) image fusion has gained significant attention for its broad application value. However, existing methods often neglect the complementary role of infrared image in restoring visible image features under hazy conditions. To address this, we propose a joint learning framework that utilizes infrared image for the restoration and fusion of hazy IR-VIS images. To mitigate the adverse effects o
RobustFormer: Noise-Robust Pre-training for images and videos
While deep learning-based models like transformers, have revolutionized time-series and vision tasks, they remain highly susceptible to noise and often overfit on noisy patterns rather than robust features. This issue is exacerbated in vision transformers, which rely on pixel-level details that can easily be corrupt. To address this, we leverage the discrete wavelet transform (DWT) for its ability to decompose into m
Convolutional neural networks (CNNs) are extremely popular and effective for image classification tasks but tend to be overly confident in their predictions. Various works have sought to quantify uncertainty associated with these models, detect out-of-distribution (OOD) inputs, or identify anomalous regions in an image, but limited work has sought to develop a holistic approach that can accurately estimate perception
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next
DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale
Time-resolved high-resolution X-ray Computed Tomography (4D $μ$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution c
Notable events recorded on this day and month across all years.